Discrete Choice Models

1. William Greene Stern School of Business New York University Discrete Choice Models

2. Part 15 Stated Preference and Revealed Preference Data

3. Panel Data • Repeated Choice Situations • Typically RP/SP constructions (experimental) • Accommodating “panel data” • Multinomial Probit [Marginal, impractical] • Latent Class • Mixed Logit

4. Revealed and Stated Preference Data • Pure RP Data • Market (ex-post, e.g., supermarket scanner data) • Individual observations • Pure SP Data • Contingent valuation • (?) Validity • Combined (Enriched) RP/SP • Mixed data • Expanded choice sets

5. Revealed Preference Data • Advantage: Actual observations on actual behavior • Disadvantage: Limited range of choice sets and attributes – does not allow analysis of switching behavior.

6. Stated Preference Data • Pure hypothetical – does the subject take it seriously? • No necessary anchor to real market situations • Vast heterogeneity across individuals

7. Pooling RP and SP Data Sets - 1 • Enrich the attribute set by replicating choices • E.g.: • RP: Bus,Car,Train (actual) • SP: Bus(1),Car(1),Train(1) Bus(2),Car(2),Train(2),… • How to combine?

8. Underlying Random Utility Model

9. Nested Logit Approach Mode RP Car Train Bus SPCar SPTrain SPBus Use a two level nested model, and constrain three IV parameters to be equal.

10. Enriched Data Set – Vehicle Choice Choosing between Conventional, Electric and LPG/CNG Vehicles in Single-Vehicle Households David A. Hensher Institute of Transport Studies School of Business The University of Sydney NSW 2006 Australia William H. Greene Department of Economics The Stern School of Business New York University New York USA 22 September 2000

11. Fuel Types Study • Conventional, Electric, Alternative • 1,400 Sydney Households • Automobile choice survey • RP + 3 SP fuel classes • Nested logit – 2 level approach – to handle the scaling issue

12. Attribute Space: Conventional

13. Attribute Space: Electric

14. Attribute Space: Alternative

15. Mixed Logit Approaches • Pivot SP choices around an RP outcome. • Scaling is handled directly in the model • Continuity across choice situations is handled by random elements of the choice structure that are constant through time • Preference weights – coefficients • Scaling parameters • Variances of random parameters • Overall scaling of utility functions

16. Generalized Mixed Logit Model Uij = j + i′xij + ′zi + ij. Uijt = j + i′xitj + ′zit + ijt. i =  + vi i = exp(-2/2 + wi) i = i + [ + i(1 - )]vi

17. Survey Instrument for Reliability Study

19. SP Study Using WTP Space

20. Generalized Mixed Logit Model One choice setting Uij = j + i′xij + ′zi + ij. Stated choice setting, multiple choices Uijt = j + i′xitj + ′zit + ijt. Random parameters i =  + vi Generalized mixed logit i = exp(-2/2 + wi) i = i + [ + i(1 - )]vi

21. Experimental Design

22. Survey Instrument

23. Conclusion THANK YOU!